Abstract
Chemical compounds can be identified through a graphical depiction, a suitable string representation, or a chemical name. A universally accepted naming scheme for chemistry was established by the International Union of Pure and Applied Chemistry (IUPAC) based on a set of rules. Due to the complexity of this ruleset a correct chemical name assignment remains challenging for human beings and there are only a few rule-based cheminformatics toolkits available that support this task in an automated manner. Here we present STOUT (SMILES-TO-IUPAC-name translator), a deep-learning neural machine translation approach to generate the IUPAC name for a given molecule from its SMILES string as well as the reverse translation, i.e. predicting the SMILES string from the IUPAC name. In both cases, the system is able to predict with an average BLEU score of about 90% and a Tanimoto similarity index of more than 0.9. Also incorrect predictions show a remarkable similarity between true and predicted compounds.
Highlights
Assigning names to chemical compounds so that an author can refer to them in the text of a scientific article, book or patent has a long history
Using the chemistry development kit (CDK), explicit hydrogens were removed from the molecules and their topological structures were converted to canonical Simplified molecular-input line-entry system (SMILES) strings
Using a Tensor Processing Units (TPU) V3-32 allows for an additional 4 times faster performance in comparison to a TPU V3-8 and is 54 times faster compared to a Graphics processing unit (GPU) V100, see Fig. 3
Summary
Assigning names to chemical compounds so that an author can refer to them in the text of a scientific article, book or patent has a long history. In the early days and even still today, such names were often chosen based on physicochemical or perceptible properties, and named after species, people, named after fictional characters, related to sex, bodily functions, death and decay, religion or legend, or other [1]. This makes it impossible to conclude from the name to the chemical structure of the compound. While in principle being human-readable, these representations are primarily designed to be understood by machines They are not commonly used in text to denominate chemical compounds for recognition by human readers, but have been incorporated into many major open-source and proprietary cheminformatics toolkits.
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